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          <h1 class="post-title" itemprop="name headline">[face_recognition中文文档] 第4节 Face Recognition API</h1>
        

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        <h1 id="Face-Recognition-API"><a href="#Face-Recognition-API" class="headerlink" title="Face Recognition API"></a>Face Recognition API</h1><ul>
<li><a href="https://face-recognition.readthedocs.io/en/latest/face_recognition.html" target="_blank" rel="external">face_recognition包</a><ul>
<li><a href="https://face-recognition.readthedocs.io/en/latest/face_recognition.html#module-face_recognition.api" target="_blank" rel="external">模块内容</a></li>
</ul>
</li>
</ul>
<hr>
<figure class="highlight routeros"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">face_recognition.api.batch_face_locations(images, <span class="attribute">number_of_times_to_upsample</span>=1, <span class="attribute">batch_size</span>=128)</span><br></pre></td></tr></table></figure>
<p><a href="https://face-recognition.readthedocs.io/en/latest/_modules/face_recognition/api.html#batch_face_locations" target="_blank" rel="external">源码</a></p>
<p>使用cnn面部检测器返回图像中二维人脸的边界框数组，如果您正在使用GPU，这可以更快的给您结果，因为GPU可以一次处理批次的图像。如果您不使用GPU，则不需要此功能。</p>
<h5 id="参数："><a href="#参数：" class="headerlink" title="参数："></a>参数：</h5><ul>
<li>images - 图像列表（每个作为numpy数组）</li>
<li>number_of_times_to_upsample - 用于对图像进行采样的次数。较高的数字找到较小的脸。</li>
<li>batch_size - 每个GPU处理批次中包含的图像数量。</li>
</ul>
<h5 id="返回："><a href="#返回：" class="headerlink" title="返回："></a>返回：</h5><p>一个可以在css（上，右，下，左）顺序中找到的人脸位置的元组列表</p>
<hr>
<figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">face_recognition<span class="selector-class">.api</span><span class="selector-class">.compare_faces</span>(known_face_encodings, face_encoding_to_check, tolerance=<span class="number">0.6</span>)</span><br></pre></td></tr></table></figure>
<p><a href="https://face-recognition.readthedocs.io/en/latest/_modules/face_recognition/api.html#compare_faces" target="_blank" rel="external">源码</a></p>
<p>将候选编码的面部编码列表进行比较，以查看它们是否匹配。</p>
<h5 id="参数：-1"><a href="#参数：-1" class="headerlink" title="参数："></a>参数：</h5><ul>
<li>known_face_encodings - 已知面部编码的列表</li>
<li>face_encoding_to_check - 与已知面部编码的列表进行比较的单面编码</li>
<li>tolerance - 面孔之间的距离要考虑多少。越小越严格， 0.6是典型的最佳性能。</li>
</ul>
<h5 id="返回：-1"><a href="#返回：-1" class="headerlink" title="返回："></a>返回：</h5><p>一个True / False值的列表，指出哪个known_face_encodings匹配要检查的面部编码</p>
<hr>
<figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">face_recognition<span class="selector-class">.api</span><span class="selector-class">.face_distance</span>(face_encodings, face_to_compare)</span><br></pre></td></tr></table></figure>
<p><a href="https://face-recognition.readthedocs.io/en/latest/_modules/face_recognition/api.html#face_distance" target="_blank" rel="external">源码</a></p>
<p>给出面部编码列表，将其与已知的面部编码进行比较，并为每个比较的人脸获得欧几里得距离。距离告诉你面孔是如何相似的。</p>
<h5 id="参数：-2"><a href="#参数：-2" class="headerlink" title="参数："></a>参数：</h5><ul>
<li>face_encodings - 要比较的面部编码列表</li>
<li>face_to_compare - 要比较的面部编码</li>
</ul>
<h5 id="返回：-2"><a href="#返回：-2" class="headerlink" title="返回："></a>返回：</h5><p>一个numpy ndarray，每个面的距离与“faces”数组的顺序相同</p>
<hr>
<figure class="highlight routeros"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">face_recognition.api.face_encodings(face_image, <span class="attribute">known_face_locations</span>=None, <span class="attribute">num_jitters</span>=1)</span><br></pre></td></tr></table></figure>
<p><a href="https://face-recognition.readthedocs.io/en/latest/_modules/face_recognition/api.html#face_encodings" target="_blank" rel="external">源码</a></p>
<p>给定图像，返回图像中每个面部的128维面部编码。</p>
<h5 id="参数：-3"><a href="#参数：-3" class="headerlink" title="参数："></a>参数：</h5><ul>
<li>face_image - 包含一个或多个面的图像</li>
<li>known_face_locations - 可选 - 如果您已经知道它们，每个面的边框。</li>
<li>num_jitters - 计算编码时重新采样多少次。更高更准确，但更慢（即100是100倍慢）</li>
</ul>
<h5 id="返回：-3"><a href="#返回：-3" class="headerlink" title="返回："></a>返回：</h5><p>128个面部编码的列表（图像中的每个脸部一个）</p>
<hr>
<figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">face_recognition<span class="selector-class">.api</span><span class="selector-class">.face_landmarks</span>(face_image, face_locations=None)</span><br></pre></td></tr></table></figure>
<p><a href="https://face-recognition.readthedocs.io/en/latest/_modules/face_recognition/api.html#face_landmarks" target="_blank" rel="external">源码</a></p>
<p>给定图像，返回图像中每个脸部的脸部特征位置（眼睛，鼻子等）的指令</p>
<h5 id="参数：-4"><a href="#参数：-4" class="headerlink" title="参数："></a>参数：</h5><ul>
<li>face_image - 要搜索的图像</li>
<li>face_locations - 可选地提供要检查的面部位置的列表。</li>
</ul>
<h5 id="返回：-4"><a href="#返回：-4" class="headerlink" title="返回："></a>返回：</h5><p>面部特征位置（眼睛，鼻子等）的列表</p>
<hr>
<figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">face_recognition<span class="selector-class">.api</span><span class="selector-class">.face_locations</span>(<span class="selector-tag">img</span>, number_of_times_to_upsample=<span class="number">1</span>, model=<span class="string">'hog'</span>)</span><br></pre></td></tr></table></figure>
<p><a href="https://face-recognition.readthedocs.io/en/latest/_modules/face_recognition/api.html#face_locations" target="_blank" rel="external">源码</a></p>
<p>返回图像中人脸的边框数组</p>
<h5 id="参数：-5"><a href="#参数：-5" class="headerlink" title="参数："></a>参数：</h5><ul>
<li>img - 一个图像（作为一个numpy数组）</li>
<li>number_of_times_to_upsample - 用于对图像进行上采样的次数多少次。较高的数字找到较小的脸。</li>
<li>model - 要使用的面部检测模型。“hog”在CPU上不太准确，但速度更快。“cnn”是一个更准确的深入学习模式，GPU / CUDA加速（如果可用）。默认为“hog”。</li>
</ul>
<h5 id="返回：-5"><a href="#返回：-5" class="headerlink" title="返回："></a>返回：</h5><p>一个可以在css（上，右，下，左）顺序中找到的表面位置的元组列表</p>
<hr>
<figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">face_recognition<span class="selector-class">.api</span><span class="selector-class">.load_image_file</span>(file, mode=<span class="string">'RGB'</span>)</span><br></pre></td></tr></table></figure>
<p><a href="https://face-recognition.readthedocs.io/en/latest/_modules/face_recognition/api.html#load_image_file" target="_blank" rel="external">源码</a></p>
<p>将图像文件（.jpg，.png等）加载到numpy数组中</p>
<h5 id="参数：-6"><a href="#参数：-6" class="headerlink" title="参数："></a>参数：</h5><ul>
<li>file - 要加载的图像文件名或文件对象</li>
<li>mode - 将图像转换为格式。只支持“RGB”（8位RGB，3声道）和“L”（黑白）。</li>
</ul>
<h5 id="返回：-6"><a href="#返回：-6" class="headerlink" title="返回："></a>返回：</h5><p>图像内容为numpy数组</p>
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              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-frown-o fa-5x" /></div>'
            } else {
              resultItems.sort(function (resultLeft, resultRight) {
                if (resultLeft.searchTextCount !== resultRight.searchTextCount) {
                  return resultRight.searchTextCount - resultLeft.searchTextCount;
                } else if (resultLeft.hitCount !== resultRight.hitCount) {
                  return resultRight.hitCount - resultLeft.hitCount;
                } else {
                  return resultRight.id - resultLeft.id;
                }
              });
              var searchResultList = '<ul class=\"search-result-list\">';
              resultItems.forEach(function (result) {
                searchResultList += result.item;
              })
              searchResultList += "</ul>";
              resultContent.innerHTML = searchResultList;
            }
          }

          if ('auto' === 'auto') {
            input.addEventListener('input', inputEventFunction);
          } else {
            $('.search-icon').click(inputEventFunction);
            input.addEventListener('keypress', function (event) {
              if (event.keyCode === 13) {
                inputEventFunction();
              }
            });
          }

          // remove loading animation
          $(".local-search-pop-overlay").remove();
          $('body').css('overflow', '');

          proceedsearch();
        }
      });
    }

    // handle and trigger popup window;
    $('.popup-trigger').click(function(e) {
      e.stopPropagation();
      if (isfetched === false) {
        searchFunc(path, 'local-search-input', 'local-search-result');
      } else {
        proceedsearch();
      };
    });

    $('.popup-btn-close').click(onPopupClose);
    $('.popup').click(function(e){
      e.stopPropagation();
    });
    $(document).on('keyup', function (event) {
      var shouldDismissSearchPopup = event.which === 27 &&
        $('.search-popup').is(':visible');
      if (shouldDismissSearchPopup) {
        onPopupClose();
      }
    });
  </script>





  

  

  

  
  

  

  

  



  
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